Consumer photo collections are all pervasive. Mining semantically meaningful information from such collections has been an area of active research in machine learning and computer vision communities. There is a large body of work focusing on problems of object recognition, detecting objects of certain types such as faces, cars, grass, water, sky, and so on. Most of this work relies on using low level vision features (such as color, texture and lines) available in the image. In the recent years, there has been an increasing focus on extracting semantically more complex information such as scene detection and activity recognition. For example, one might want to cluster pictures based on if they were taken outdoors or indoors, or separate work pictures from leisure pictures. Solution to such problems primarily relies on using the derived features such as people present in the image, presence or absence of certain kinds of objects in the image and so on. Typically, power of collective inference is used in such scenarios. For example, it may be difficult to tell for a particular picture if it is work or leisure, but looking at other pictures which are similar in location and time, it might become easier to make the same prediction. This line of research aims to revolutionize the way people perceive the digital photo collection—from a bunch of pixel values to highly complex and meaningful objects which can be queried for information or automatically organized in ways which are meaningful to the user.
Taking semantic understanding a step further, humans have the ability to infer the relationships between people appearing in the same picture after observing a sufficient number of pictures: are they families' members, friends, just acquaintances, or merely strangers who happen to be in the same place at the same time. In other words, consumer photos are usually not taken in coincidence with strangers but often with friends and families. Detecting or predicting such relationships can be an important step towards building intelligent cameras as well as intelligent image management systems.